Segmentation and Classification of Cervix Lesions by Pattern and Texture Analysis

This work aims at automated segmentation of major lesions observed in early stages of cervical cancer which is the second most common cancer among women worldwide. The purpose of segmentation is to automatically determine the location for a biopsy to be taken for diagnosis, a process that is currently done manually. The acetowhite region, a major indicator of abnormality in the cervix image, is first segmented by using a nonconvex optimization approach. Within the acetowhite region, other abnormal features such as the mosaic patterns are then automatically classified from nonmosaic regions by texture analysis. The abnormal features are obtained from skeletonized vascular structures uniquely representing typical vascularity embedded in the normal and abnormal regions extracted by a series of mathematical morphological operations. From the extracted vascular structure a texture feature is identified which clearly distinguishes between the normal and abnormal regions and is used for automated segmentation of the mosaic patterns using fuzzy c-means. Analysis and interpretation of cervix images are important in early detection of cervical lesions. Automated image analysis provides quantitative description of lesions thus less subjective variability in monitoring of chronic lesions so that cervical cancer can be treated effectively at its onset